import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns 
import warnings
warnings.filterwarnings('ignore')

# reading the dataset and storing it in a dataframe
df = pd.read_csv('311_Service_Requests_from_2010_to_Present.csv', low_memory =False)

# checking the dataset
df.head()

	Unique Key	Created Date	Closed Date	Agency	Agency Name	Complaint Type	Descriptor	Location Type	Incident Zip	Incident Address	...	Bridge Highway Name	Bridge Highway Direction	Road Ramp	Bridge Highway Segment	Garage Lot Name	Ferry Direction	Ferry Terminal Name	Latitude	Longitude	Location
0	32310363	12/31/2015 11:59:45 PM	01-01-16 0:55	NYPD	New York City Police Department	Noise - Street/Sidewalk	Loud Music/Party	Street/Sidewalk	10034.0	71 VERMILYEA AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.865682	-73.923501	(40.86568153633767, -73.92350095571744)
1	32309934	12/31/2015 11:59:44 PM	01-01-16 1:26	NYPD	New York City Police Department	Blocked Driveway	No Access	Street/Sidewalk	11105.0	27-07 23 AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.775945	-73.915094	(40.775945312321085, -73.91509393898605)
2	32309159	12/31/2015 11:59:29 PM	01-01-16 4:51	NYPD	New York City Police Department	Blocked Driveway	No Access	Street/Sidewalk	10458.0	2897 VALENTINE AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.870325	-73.888525	(40.870324522111424, -73.88852464418646)
3	32305098	12/31/2015 11:57:46 PM	01-01-16 7:43	NYPD	New York City Police Department	Illegal Parking	Commercial Overnight Parking	Street/Sidewalk	10461.0	2940 BAISLEY AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.835994	-73.828379	(40.83599404683083, -73.82837939584206)
4	32306529	12/31/2015 11:56:58 PM	01-01-16 3:24	NYPD	New York City Police Department	Illegal Parking	Blocked Sidewalk	Street/Sidewalk	11373.0	87-14 57 ROAD	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.733060	-73.874170	(40.733059618956815, -73.87416975810375)

5 rows × 53 columns

# checking the sahpe of dataset 
df.shape

(300698, 53)

df['Created Date'] = pd.to_datetime(df['Created Date']) # coverting datetime object to time delta 

df['Closed Date'] = pd.to_datetime(df['Closed Date']) # coverting datetime object to time delta

df.head()

	Unique Key	Created Date	Closed Date	Agency	Agency Name	Complaint Type	Descriptor	Location Type	Incident Zip	Incident Address	...	Bridge Highway Name	Bridge Highway Direction	Road Ramp	Bridge Highway Segment	Garage Lot Name	Ferry Direction	Ferry Terminal Name	Latitude	Longitude	Location
0	32310363	2015-12-31 23:59:45	2016-01-01 00:55:00	NYPD	New York City Police Department	Noise - Street/Sidewalk	Loud Music/Party	Street/Sidewalk	10034.0	71 VERMILYEA AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.865682	-73.923501	(40.86568153633767, -73.92350095571744)
1	32309934	2015-12-31 23:59:44	2016-01-01 01:26:00	NYPD	New York City Police Department	Blocked Driveway	No Access	Street/Sidewalk	11105.0	27-07 23 AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.775945	-73.915094	(40.775945312321085, -73.91509393898605)
2	32309159	2015-12-31 23:59:29	2016-01-01 04:51:00	NYPD	New York City Police Department	Blocked Driveway	No Access	Street/Sidewalk	10458.0	2897 VALENTINE AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.870325	-73.888525	(40.870324522111424, -73.88852464418646)
3	32305098	2015-12-31 23:57:46	2016-01-01 07:43:00	NYPD	New York City Police Department	Illegal Parking	Commercial Overnight Parking	Street/Sidewalk	10461.0	2940 BAISLEY AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.835994	-73.828379	(40.83599404683083, -73.82837939584206)
4	32306529	2015-12-31 23:56:58	2016-01-01 03:24:00	NYPD	New York City Police Department	Illegal Parking	Blocked Sidewalk	Street/Sidewalk	11373.0	87-14 57 ROAD	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	40.733060	-73.874170	(40.733059618956815, -73.87416975810375)

5 rows × 53 columns

df['Request_Closing_Time'] = df['Closed Date'] - df['Created Date'] # creating a column to store time taken to close the complain

df['Request_Closing_Time'] = df['Request_Closing_Time'].dt.seconds #converting time to seconds

df['Request_Closing_Time'] = df['Request_Closing_Time']/3600 # converting time to hours

df.head()

	Unique Key	Created Date	Closed Date	Agency	Agency Name	Complaint Type	Descriptor	Location Type	Incident Zip	Incident Address	...	Bridge Highway Direction	Road Ramp	Bridge Highway Segment	Garage Lot Name	Ferry Direction	Ferry Terminal Name	Latitude	Longitude	Location	Request_Closing_Time
0	32310363	2015-12-31 23:59:45	2016-01-01 00:55:00	NYPD	New York City Police Department	Noise - Street/Sidewalk	Loud Music/Party	Street/Sidewalk	10034.0	71 VERMILYEA AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	40.865682	-73.923501	(40.86568153633767, -73.92350095571744)	0.920833
1	32309934	2015-12-31 23:59:44	2016-01-01 01:26:00	NYPD	New York City Police Department	Blocked Driveway	No Access	Street/Sidewalk	11105.0	27-07 23 AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	40.775945	-73.915094	(40.775945312321085, -73.91509393898605)	1.437778
2	32309159	2015-12-31 23:59:29	2016-01-01 04:51:00	NYPD	New York City Police Department	Blocked Driveway	No Access	Street/Sidewalk	10458.0	2897 VALENTINE AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	40.870325	-73.888525	(40.870324522111424, -73.88852464418646)	4.858611
3	32305098	2015-12-31 23:57:46	2016-01-01 07:43:00	NYPD	New York City Police Department	Illegal Parking	Commercial Overnight Parking	Street/Sidewalk	10461.0	2940 BAISLEY AVENUE	...	NaN	NaN	NaN	NaN	NaN	NaN	40.835994	-73.828379	(40.83599404683083, -73.82837939584206)	7.753889
4	32306529	2015-12-31 23:56:58	2016-01-01 03:24:00	NYPD	New York City Police Department	Illegal Parking	Blocked Sidewalk	Street/Sidewalk	11373.0	87-14 57 ROAD	...	NaN	NaN	NaN	NaN	NaN	NaN	40.733060	-73.874170	(40.733059618956815, -73.87416975810375)	3.450556

5 rows × 54 columns

df.columns # Checking 

Index(['Unique Key', 'Created Date', 'Closed Date', 'Agency', 'Agency Name',
       'Complaint Type', 'Descriptor', 'Location Type', 'Incident Zip',
       'Incident Address', 'Street Name', 'Cross Street 1', 'Cross Street 2',
       'Intersection Street 1', 'Intersection Street 2', 'Address Type',
       'City', 'Landmark', 'Facility Type', 'Status', 'Due Date',
       'Resolution Description', 'Resolution Action Updated Date',
       'Community Board', 'Borough', 'X Coordinate (State Plane)',
       'Y Coordinate (State Plane)', 'Park Facility Name', 'Park Borough',
       'School Name', 'School Number', 'School Region', 'School Code',
       'School Phone Number', 'School Address', 'School City', 'School State',
       'School Zip', 'School Not Found', 'School or Citywide Complaint',
       'Vehicle Type', 'Taxi Company Borough', 'Taxi Pick Up Location',
       'Bridge Highway Name', 'Bridge Highway Direction', 'Road Ramp',
       'Bridge Highway Segment', 'Garage Lot Name', 'Ferry Direction',
       'Ferry Terminal Name', 'Latitude', 'Longitude', 'Location',
       'Request_Closing_Time'],
      dtype='object')

Complaints = df[['City','Complaint Type','Location Type','Status','Borough','Latitude','Longitude', 'Request_Closing_Time']]
Complaints.head()

	City	Complaint Type	Location Type	Status	Borough	Latitude	Longitude	Request_Closing_Time
0	NEW YORK	Noise - Street/Sidewalk	Street/Sidewalk	Closed	MANHATTAN	40.865682	-73.923501	0.920833
1	ASTORIA	Blocked Driveway	Street/Sidewalk	Closed	QUEENS	40.775945	-73.915094	1.437778
2	BRONX	Blocked Driveway	Street/Sidewalk	Closed	BRONX	40.870325	-73.888525	4.858611
3	BRONX	Illegal Parking	Street/Sidewalk	Closed	BRONX	40.835994	-73.828379	7.753889
4	ELMHURST	Illegal Parking	Street/Sidewalk	Closed	QUEENS	40.733060	-73.874170	3.450556

status = Complaints['Status'].value_counts() # getting complaint status 

#Top 10 Complaint Types
groupby_complaint_type = Complaints['Complaint Type'].value_counts().nlargest(10)
groupby_complaint_type # getting highest complaints 

Blocked Driveway           77044
Illegal Parking            75361
Noise - Street/Sidewalk    48612
Noise - Commercial         35577
Derelict Vehicle           17718
Noise - Vehicle            17083
Animal Abuse                7778
Traffic                     4498
Homeless Encampment         4416
Noise - Park                4042
Name: Complaint Type, dtype: int64

# Graph representing complaints under each category 
Complaints['Complaint Type'].value_counts().plot(kind ='bar', alpha=0.6 ,figsize =(15,10))
plt.title("No. of Complaints Recieved Under Each Category", fontsize = 20, color='red',pad ='3.0')
plt.xlabel('Complaint Types ->', fontsize = 15)
plt.ylabel('No. of compliants ->' , fontsize = 15)
plt.show()

Conclusion

Top five complaints registrerd are:

• Blocked Driveway -77044
• Illegal Parking -75361
• Noise-Street/Sidewalk-48612
• Noise-Commercial -35577
• Derelict Vehicle -17718

#Top 10 cities with complaints
groupby_city = Complaints['City'].value_counts().nlargest(10)
groupby_city #getting city with highest complaints 

BROOKLYN         98307
NEW YORK         65994
BRONX            40702
STATEN ISLAND    12343
JAMAICA           7296
ASTORIA           6330
FLUSHING          5971
RIDGEWOOD         5163
CORONA            4295
WOODSIDE          3544
Name: City, dtype: int64

# Graph representing cities and complaints registered 
Complaints['City'].value_counts().plot(kind ='bar', alpha=0.6 ,figsize =(16,10),color='red')
plt.title("No. of Complaints Recieved for Each City", fontsize = 20, color='red',pad ='3.0')
plt.xlabel('City', fontsize = 15)
plt.ylabel('No. of complaints' , fontsize = 15)
plt.show()

Conclusion

Top 5 cities as per the number of complaints recieved:

• Brooklyn (98307)
• New York (65994)
• Bronx (40702)
• Staten Island (12343)
• Jamaica (7296)

Top_city = Complaints[Complaints['City']== 'BROOKLYN']
Blocked_Driveway = Top_city[Top_city['Complaint Type'] == 'Blocked Driveway']
Blocked_Driveway.head()

	City	Complaint Type	Location Type	Status	Borough	Latitude	Longitude	Request_Closing_Time
9	BROOKLYN	Blocked Driveway	Street/Sidewalk	Closed	BROOKLYN	40.623793	-73.999539	1.383889
36	BROOKLYN	Blocked Driveway	Street/Sidewalk	Closed	BROOKLYN	40.670843	-73.935556	0.312500
102	BROOKLYN	Blocked Driveway	Street/Sidewalk	Closed	BROOKLYN	40.689303	-73.943395	0.546667
110	BROOKLYN	Blocked Driveway	Street/Sidewalk	Closed	BROOKLYN	40.638963	-73.911903	4.416667
117	BROOKLYN	Blocked Driveway	Street/Sidewalk	Closed	BROOKLYN	40.630849	-73.901790	4.578889

Blocked_Driveway['Complaint Type'].value_counts()

Blocked Driveway    28148
Name: Complaint Type, dtype: int64

Cluster Map

Preparing a cluster map for the Blocked Driveway in Brooklyn

import folium
import math
from folium.plugins import MarkerCluster
import plotly.graph_objects as go
import plotly.express as px
import datetime
from plotly.subplots import make_subplots

Cluster Map (Blocked_Driveway , Brooklyn)

# Base Map
Blyn = (40.6782,-73.9442)
m= folium.Map(location = Blyn, tile = 'stamen terrain', zoom_start =12)

# Cluster Map of Blocked Driveway compliants in Brooklyn  
mc = MarkerCluster()
for idx,row in Blocked_Driveway.iterrows():
    if not math.isnan(row['Longitude']) and not math.isnan(row['Latitude']):
        mc.add_child(folium.Marker([row['Latitude'],row['Longitude']]))
m.add_child(mc)

Make this Notebook Trusted to load map: File -> Trust Notebook

Test_df = Complaints[['Complaint Type','Request_Closing_Time']]
Test_df.head()

	Complaint Type	Request_Closing_Time
0	Noise - Street/Sidewalk	0.920833
1	Blocked Driveway	1.437778
2	Blocked Driveway	4.858611
3	Illegal Parking	7.753889
4	Illegal Parking	3.450556

Test_df.isnull().sum() #checking null or NaN values in the dataframe 

Complaint Type             0
Request_Closing_Time    2164
dtype: int64

# Test_final = Test_df.dropna() # dropping the rows with NaN values in Request_Closing_Time
# Test_final.isnull().sum() # Checking if NaN values are completely removed 

# Average time to address each complaint types
Test_df1 = Test_df.groupby(by= 'Complaint Type').mean()
Test_df1.head()

	Request_Closing_Time
Complaint Type
Agency Issues	5.260324
Animal Abuse	4.626220
Animal in a Park	0.834722
Bike/Roller/Skate Chronic	3.540047
Blocked Driveway	4.384702

Test_df1.sort_values(by='Request_Closing_Time', ascending = False)
Test_df1.head()

	Request_Closing_Time
Complaint Type
Agency Issues	5.260324
Animal Abuse	4.626220
Animal in a Park	0.834722
Bike/Roller/Skate Chronic	3.540047
Blocked Driveway	4.384702

# Graph representing cities and complaints registered 
Test_df1.sort_values(by='Request_Closing_Time').plot(kind ='bar', alpha=0.6 ,figsize =(16,12),color='green')
plt.title("Average Closing Time for Complain Types", fontsize = 20, color='red',pad ='3.0')
plt.xlabel('Complaint Type', fontsize = 15)
plt.ylabel('Request_Closing_Time' , fontsize = 15)
plt.show()

#Top 10 comaplints with largest average closing time 
groupby_closing_time = Test_df1['Request_Closing_Time'].nlargest(5)
Avg_closing_time = pd.DataFrame(groupby_closing_time)
Avg_closing_time

	Request_Closing_Time
Complaint Type
Graffiti	5.664526
Derelict Vehicle	5.588838
Agency Issues	5.260324
Animal Abuse	4.626220
Blocked Driveway	4.384702

#Top 10 comaplints with less average closing time 
groupby_closing_time = Test_df1['Request_Closing_Time'].nsmallest(5)
Avg_closing_time = pd.DataFrame(groupby_closing_time)
Avg_closing_time

	Request_Closing_Time
Complaint Type
Animal in a Park	0.834722
Posting Advertisement	1.938766
Illegal Fireworks	2.618282
Noise - House of Worship	2.960790
Noise - Commercial	2.991226

Conclusion

• Graffiti has the highest Request_Closing_Time (5.664526 hrs)
• Animal in park has the lowest Request_Closing_Time (0.834722 hrs)

Borough_df =Complaints[['Borough','Complaint Type']]
Borough_df.head()

	Borough	Complaint Type
0	MANHATTAN	Noise - Street/Sidewalk
1	QUEENS	Blocked Driveway
2	BRONX	Blocked Driveway
3	BRONX	Illegal Parking
4	QUEENS	Illegal Parking

# Graph representing Borough and complaints registered 
Borough_df['Borough'].value_counts().plot(kind ='bar', alpha=0.6 ,figsize =(16,10),color='indigo')
plt.title("No. of Complaints Recived for Each Borough", fontsize = 20, color='red',pad ='3.0')
plt.xlabel('Borough', fontsize = 15)
plt.ylabel('No. of complaints' , fontsize = 15)
plt.show()

# Order of the complaint types based on the average ‘Request_Closing_Time’, grouping them for different locations.
df3 = Complaints[['City','Complaint Type','Request_Closing_Time']]
df4 = df3.groupby(by=['City','Complaint Type']).mean()
df4.sort_values(by=['City','Request_Closing_Time','Complaint Type'])

		Request_Closing_Time
City	Complaint Type
ARVERNE	Drinking	0.238611
	Vending	0.483333
	Urinating in Public	0.691944
	Panhandling	1.033333
	Noise - Park	1.283333
...	...	...
Woodside	Noise - Commercial	2.394167
	Noise - Street/Sidewalk	3.411278
	Derelict Vehicle	4.966667
	Illegal Parking	4.979203
	Blocked Driveway	6.406212

764 rows × 1 columns

Hypothesis Testing

Case1

• H0 --> Average Response_Closing_Time across the Complaint Types is same
• H1 --> Average Response_Closing_Time across the Complaint Types isn't same

# Importing requires libraries for hypothesis testing
import pingouin as pg
from scipy.stats import chi2_contingency,chi2

Complaint_Response_Time = Complaints[['Complaint Type','Request_Closing_Time']]

aov = pg.anova(dv = 'Request_Closing_Time', between ='Complaint Type', data = Complaint_Response_Time, detailed = True)
aov

	Source	SS	DF	MS	F	p-unc	np2
0	Complaint Type	1.407689e+05	22	6398.586790	443.372575	0.0	0.031642
1	Within	4.307999e+06	298511	14.431625	NaN	NaN	NaN

• The one-way ANOVA showed that average Response_Closing_Time across the Complaint Type isn't same, F(22, 298511) = 443.372575, p = 0.0.
• So, we regecting the null hypothesis .

---

Case 2

• H0 --> The type of complaint or service requested and location aren't related
• H1 --> The type of complaint or service requested and location are related

# creating a new dataframe with Complaint Type and Location Type 
group = Complaints[['Location Type','Complaint Type']]
group_count = group.count()

# checking NaN values 
chi_test = group.fillna(value=0)
chi_test.head()

	Location Type	Complaint Type
0	Street/Sidewalk	Noise - Street/Sidewalk
1	Street/Sidewalk	Blocked Driveway
2	Street/Sidewalk	Blocked Driveway
3	Street/Sidewalk	Illegal Parking
4	Street/Sidewalk	Illegal Parking

# checking size of dataset
chi_test.shape

(300698, 2)

# # dropping NaN values
# Chi_test = df_6.dropna()
# Chi_test.head()

# selecting field to test for the association 
myField1 = chi_test['Location Type']
myField2 = chi_test['Complaint Type']

#To get a quick look at the counts from these we can use Pandas 'crosstab'
myCrosstable = pd.crosstab(myField1,myField2)
myCrosstable.head()

Complaint Type	Agency Issues	Animal Abuse	Animal in a Park	Bike/Roller/Skate Chronic	Blocked Driveway	Derelict Vehicle	Disorderly Youth	Drinking	Ferry Complaint	Graffiti	...	Noise - House of Worship	Noise - Park	Noise - Street/Sidewalk	Noise - Vehicle	Panhandling	Posting Advertisement	Squeegee	Traffic	Urinating in Public	Vending
Location Type
0	6	3	0	0	37	8	0	1	0	0	...	2	1	11	3	0	1	0	5	1	2
Bridge	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
Club/Bar/Restaurant	0	0	0	0	0	0	0	366	0	0	...	0	0	0	0	0	0	0	0	21	0
Commercial	0	62	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
Ferry	0	0	0	0	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0

5 rows × 24 columns

myCrosstable.shape

(19, 24)

chi, pval, dof, exp = chi2_contingency(myCrosstable)
print('p-value is: ', pval)
significance = 0.05
p = 1 - significance
critical_value = chi2.ppf(p, dof)

print('chi=%.6f, critical value=%.6f\n' % (chi, critical_value))

if chi > critical_value:
    print("""At %.2f level of significance, we reject the null hypotheses and accept H1.They are related.""" % (significance))
else:
    print("""At %.2f level of significance, we accept the null hypotheses. They aren't related.""" % (significance))

p-value is:  0.0
chi=1652072.540954, critical value=462.440110

At 0.05 level of significance, we reject the null hypotheses and accept H1.They are related.

from scipy import stats 

alpha = stats.chi2.cdf(1652072.540954,414)
alpha

1.0